Linear and Parametric Microphone Array Processing - =1=Part II

113 Pages·2013·44.17 MB·English

Linear and Parametric Microphone Array Processing
Part II: Linear Spatial Processing
Emanu¨el A.P. Habets1 and Sharon Gannot2
1InternationalAudioLaboratoriesErlangen,Germany
2FacultyofEngineering,Bar-IlanUniversity,Israel
ICASSP 2013, Vancouver, Canada
E.A.P.Habets(FAU)andS.Gannot(BIU) LinearandParametricMic.ArrayProc. ICASSP2013 1/113
Outline
1 Introduction
2 Array Processing Preliminaries
3 Problem Formulation
4 Optimal Beamforming Criteria & Solutions
5 The GSC Implementation
6 ATF & RTF Estimation
7 Postfilter
8 CTF vs. MTF
9 Dynamic Scenario
10 Binaural LCMV
11 The Speech and Acoustics Lab BIU
12 Bibliography
E.A.P.Habets(FAU)andS.Gannot(BIU) LinearandParametricMic.ArrayProc. ICASSP2013 2/113
Introduction Literature
Linear Spatial Noise Reduction Techniques I
Families of Methods
1 Fixed beamforming Combine the microphone signals using a
time-invariant filter-and-sum operation (data-independent)
.
[JanandFlanagan,1996];[DocloandMoonen,2003]
2 Blind Source Separation (BSS) Considers the received signals at the
microphones as a mixture of all sound sources filtered by the RIRs.
Utilizes Independent Component Analysis (ICA) techniques
.
[Makinoetal.,2007];TRINICON,[Buchneretal.,2004]
3 Adaptive Beamforming Combine the spatial focusing of fixed
beamformers with adaptive suppression of (spectrally and spatially
time-varying) background noise
.
Generalreading:[Coxetal.,1987];[VanVeenandBuckley,1988];[VanTrees,2002]
E.A.P.Habets(FAU)andS.Gannot(BIU) LinearandParametricMic.ArrayProc. ICASSP2013 3/113
Introduction Literature
Linear Spatial Noise Reduction Techniques II
Some Criteria
1 Adaptive optimization [SondhiandElko,1986];[KanedaandOhga,1986];
.
[BrandsteinandWard,2001]
2 Minimum variance distortionless response (MVDR) and GSC
[VanCompernolle,1990];[AffesandGrenier,1997];[Nordholmetal.,1993];[Hoshuyamaetal.,1999];
.
[Gannotetal.,2001];[Herbordt,2005];[GannotandCohen,2008]
3 Minimum mean square error (MMSE) - GSVD based spatial Wiener
filter .
[DocloandMoonen,2002a]
4 Speech distortion weighted multichannel Wiener filter (SDW-MWF)
.
[DocloandMoonen,2002b];[Sprietetal.,2004];[Docloetal.,2005]
5 Maximum signal to noise ratio (SNR) [WarsitzandHaeb-Umbach,2007].
6 Linearly constrained minimum variance (LCMV) [Markovichetal.,2009].
E.A.P.Habets(FAU)andS.Gannot(BIU) LinearandParametricMic.ArrayProc. ICASSP2013 4/113
Introduction Literature
Linear Spatial Noise Reduction Techniques III
Some Books
1 Acoustic signal processing for telecommunication [GayandBenesty,2000].
2 Microphone Arrays: Signal Processing Techniques and Applications
.
[BrandsteinandWard,2001]
3 Speech Enhancement [Benestyetal.,2005].
4 Blind speech separation [Makinoetal.,2007].
5 Microphone Array Signal Processing [Benestyetal.,2008a].
6 Springer handbook of speech processing [Benestyetal.,2008b].
7 Handbook on array processing and sensor networks [HaykinandLiu,2010].
8 Speech processing in modern communication: Challenges and
perspectives .
[Cohenetal.,2010]
E.A.P.Habets(FAU)andS.Gannot(BIU) LinearandParametricMic.ArrayProc. ICASSP2013 5/113
ArrayProcessingPreliminaries
Spatial Filters
Beamforming: Filter and Sum
y(t) = wH(t)z(t).
z t w
0 0
z t w
1 1  yt
z t w
M1 M1
w: M 1 beamforming vector of filters (or just gains).
×
E.A.P.Habets(FAU)andS.Gannot(BIU) LinearandParametricMic.ArrayProc. ICASSP2013 6/113
ArrayProcessingPreliminaries
Array Processing
Preliminaries
Narrow-band Signal
Far‐Field Wave stej0t
M 1
(cid:88)−
y(t) = w ejω0(t τm)
m∗ −
m=0
M 1
 = ejω0t (cid:88)− wm∗e−jω0(dcocs(θ))m
m=0
d M 1
zM1t z1t z0t = ejω0t (cid:88)− wm∗e−j2πλd0 cos(θ)m
m=0
Beampattern is the DTFT of the weights
(cid:16) (cid:17)
y(t) = ejω0tW d ;cos(θ)
λ0
E.A.P.Habets(FAU)andS.Gannot(BIU) LinearandParametricMic.ArrayProc. ICASSP2013 7/113
ArrayProcessingPreliminaries
The Delay & Sum Beamformer
Uniform Linear Array (ULA)
w = 1 ; m = 0,...,M 1.
m M −
For simplicity, assume symmetric array.
Steered to cos(θ ).
0
Beampattern:
(cid:16) (cid:17)
sin M2π d (cos(θ) cos(θ ))
B(θ) = 1 2 λ0 − 0
(cid:16) (cid:17)
M · sin 12π d (cos(θ) cos(θ ))
2 λ0 − 0
Beamformers
Discriminate between angles.
Can be steered by setting w.
Depends on the ratio d .
λ0
E.A.P.Habets(FAU)andS.Gannot(BIU) LinearandParametricMic.ArrayProc. ICASSP2013 8/113
ArrayProcessingPreliminaries
Beampattern
90 1 90 1 90 1
120 60 120 60 120 60
0.8 0.8 0.8
0.6 0.6 0.6
150 30 150 30 150 30
0.4 0.4 0.4
0.2 0.2 0.2
180 0 180 0 180 0
210 330 210 330 210 330
240 300 240 300 240 300
270 270 270
(a) θ0=90o;λd0 = 12 (b) θ0=0o;λd0 = 21 (c) θ0=40o;λd0 = 12
90 1 90 1
120 60 120 60
0.8 0.8
0.6 0.6
150 30 150 30
0.4 0.4
0.2 0.2
180 0 180 0
210 330 210 330
240 300 240 300
270 270
(d) θ0=90o;λd0 = 312 (e) θ0=90o;λd0 = 41
E.A.P.Habets(FAU)andS.Gannot(BIU) LinearandParametricMic.ArrayProc. ICASSP2013 9/113
ArrayProcessingPreliminaries
Additional Control on the Beampattern
0°
0 dB
315° −10 dB 45°
−20 dB
−30 dB
270° −40 dB 90°
225° 135°
180°
10 microphone uniform linear array.
2 Desired sources in green and 2 interfering sources in red.
Can be obtained by applying the LCMV criterion.
E.A.P.Habets(FAU)andS.Gannot(BIU) LinearandParametricMic.ArrayProc. ICASSP2013 10/113